Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations90785
Missing cells190360
Missing cells (%)6.8%
Duplicate rows7061
Duplicate rows (%)7.8%
Total size in memory21.5 MiB
Average record size in memory248.0 B

Variable types

Numeric7
Categorical21
Boolean3

Alerts

Dataset has 7061 (7.8%) duplicate rowsDuplicates
@30daymortality is highly overall correlated with thirtydaymortalityHigh correlation
AGE is highly overall correlated with AGEcategory and 1 other fieldsHigh correlation
AGEcategory is highly overall correlated with AGE and 1 other fieldsHigh correlation
AGEcategoryOriginal is highly overall correlated with AGE and 1 other fieldsHigh correlation
Anemia category is highly overall correlated with AnemiacategorybinnedHigh correlation
Anemiacategorybinned is highly overall correlated with Anemia category and 3 other fieldsHigh correlation
CHFRCRICategory is highly overall correlated with CVARCRICategory and 2 other fieldsHigh correlation
CVARCRICategory is highly overall correlated with CHFRCRICategory and 2 other fieldsHigh correlation
CreatinineRCRICategory is highly overall correlated with GradeofKidneyCategory and 1 other fieldsHigh correlation
DMinsulinRCRICategory is highly overall correlated with CHFRCRICategory and 2 other fieldsHigh correlation
DaysbetweenDeathandoperation is highly overall correlated with MortalityHigh correlation
GradeofKidneyCategory is highly overall correlated with Anemiacategorybinned and 2 other fieldsHigh correlation
GradeofKidneydisease is highly overall correlated with Anemiacategorybinned and 2 other fieldsHigh correlation
IHDRCRICategory is highly overall correlated with CHFRCRICategory and 2 other fieldsHigh correlation
Intraop is highly overall correlated with TransfusionIntraandpostopCategoryHigh correlation
Mortality is highly overall correlated with DaysbetweenDeathandoperationHigh correlation
Preoptransfusionwithin30days is highly overall correlated with TransfusionintraandpostopHigh correlation
RDW15.7 is highly overall correlated with AnemiacategorybinnedHigh correlation
TransfusionIntraandpostopCategory is highly overall correlated with IntraopHigh correlation
Transfusionintraandpostop is highly overall correlated with Preoptransfusionwithin30daysHigh correlation
thirtydaymortality is highly overall correlated with @30daymortalityHigh correlation
@30daymortality is highly imbalanced (94.8%)Imbalance
Intraop is highly imbalanced (68.8%)Imbalance
TransfusionIntraandpostopCategory is highly imbalanced (77.8%)Imbalance
Mortality is highly imbalanced (66.6%)Imbalance
thirtydaymortality is highly imbalanced (94.8%)Imbalance
ICUAdmgt24h is highly imbalanced (89.4%)Imbalance
RCRI score has 27424 (30.2%) missing valuesMissing
Anemia category has 4038 (4.4%) missing valuesMissing
PreopEGFRMDRD has 10830 (11.9%) missing valuesMissing
DaysbetweenDeathandoperation has 85190 (93.8%) missing valuesMissing
Anemiacategorybinned has 62878 (69.3%) missing valuesMissing
Postopwithin30days is highly skewed (γ1 = 29.85449206)Skewed
RCRI score has 47385 (52.2%) zerosZeros
Preoptransfusionwithin30days has 88905 (97.9%) zerosZeros
Postopwithin30days has 89842 (99.0%) zerosZeros
Transfusionintraandpostop has 85664 (94.4%) zerosZeros

Reproduction

Analysis started2024-08-02 08:32:40.338714
Analysis finished2024-08-02 08:32:47.854284
Duration7.52 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

AGE
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.253225
Minimum18
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:47.890239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q139
median54
Q365
95-th percentile79
Maximum103
Range85
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.087307
Coefficient of variation (CV)0.32700962
Kurtosis-0.85931786
Mean52.253225
Median Absolute Deviation (MAD)13
Skewness-0.11031944
Sum4743809
Variance291.97607
MonotonicityNot monotonic
2024-08-02T16:32:47.937293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 2104
 
2.3%
65 2103
 
2.3%
64 2033
 
2.2%
63 1999
 
2.2%
60 1989
 
2.2%
57 1984
 
2.2%
58 1976
 
2.2%
66 1959
 
2.2%
62 1947
 
2.1%
59 1945
 
2.1%
Other values (76) 70746
77.9%
ValueCountFrequency (%)
18 489
0.5%
19 638
0.7%
20 815
0.9%
21 894
1.0%
22 891
1.0%
23 829
0.9%
24 928
1.0%
25 982
1.1%
26 1044
1.1%
27 1138
1.3%
ValueCountFrequency (%)
103 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 2
 
< 0.1%
99 4
 
< 0.1%
98 9
 
< 0.1%
97 6
 
< 0.1%
96 19
< 0.1%
95 25
< 0.1%
94 26
< 0.1%

GENDER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
FEMALE
48708 
MALE
42077 

Length

Max length6
Median length6
Mean length5.0730407
Min length4

Characters and Unicode

Total characters460556
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEMALE
2nd rowFEMALE
3rd rowFEMALE
4th rowMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
FEMALE 48708
53.7%
MALE 42077
46.3%

Length

2024-08-02T16:32:47.983701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:48.022105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
female 48708
53.7%
male 42077
46.3%

Most occurring characters

ValueCountFrequency (%)
E 139493
30.3%
M 90785
19.7%
A 90785
19.7%
L 90785
19.7%
F 48708
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 460556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 139493
30.3%
M 90785
19.7%
A 90785
19.7%
L 90785
19.7%
F 48708
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 460556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 139493
30.3%
M 90785
19.7%
A 90785
19.7%
L 90785
19.7%
F 48708
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 460556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 139493
30.3%
M 90785
19.7%
A 90785
19.7%
L 90785
19.7%
F 48708
 
10.6%

RCRI score
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing27424
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean0.32229605
Minimum0
Maximum6
Zeros47385
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:48.050496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6347894
Coefficient of variation (CV)1.9695848
Kurtosis7.307766
Mean0.32229605
Median Absolute Deviation (MAD)0
Skewness2.4074083
Sum20421
Variance0.40295758
MonotonicityNot monotonic
2024-08-02T16:32:48.084281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 47385
52.2%
1 12653
 
13.9%
2 2441
 
2.7%
3 679
 
0.7%
4 168
 
0.2%
5 33
 
< 0.1%
6 2
 
< 0.1%
(Missing) 27424
30.2%
ValueCountFrequency (%)
0 47385
52.2%
1 12653
 
13.9%
2 2441
 
2.7%
3 679
 
0.7%
4 168
 
0.2%
5 33
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 33
 
< 0.1%
4 168
 
0.2%
3 679
 
0.7%
2 2441
 
2.7%
1 12653
 
13.9%
0 47385
52.2%

Anemia category
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing4038
Missing (%)4.4%
Memory size709.4 KiB
none
62878 
mild
13006 
moderate
10439 
severe
 
424

Length

Max length8
Median length4
Mean length4.4911294
Min length4

Characters and Unicode

Total characters389592
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rowmild
3rd rowmoderate
4th rowmild
5th rownone

Common Values

ValueCountFrequency (%)
none 62878
69.3%
mild 13006
 
14.3%
moderate 10439
 
11.5%
severe 424
 
0.5%
(Missing) 4038
 
4.4%

Length

2024-08-02T16:32:48.127527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:48.168690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
none 62878
72.5%
mild 13006
 
15.0%
moderate 10439
 
12.0%
severe 424
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 125756
32.3%
e 85028
21.8%
o 73317
18.8%
m 23445
 
6.0%
d 23445
 
6.0%
i 13006
 
3.3%
l 13006
 
3.3%
r 10863
 
2.8%
a 10439
 
2.7%
t 10439
 
2.7%
Other values (2) 848
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 389592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 125756
32.3%
e 85028
21.8%
o 73317
18.8%
m 23445
 
6.0%
d 23445
 
6.0%
i 13006
 
3.3%
l 13006
 
3.3%
r 10863
 
2.8%
a 10439
 
2.7%
t 10439
 
2.7%
Other values (2) 848
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 389592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 125756
32.3%
e 85028
21.8%
o 73317
18.8%
m 23445
 
6.0%
d 23445
 
6.0%
i 13006
 
3.3%
l 13006
 
3.3%
r 10863
 
2.8%
a 10439
 
2.7%
t 10439
 
2.7%
Other values (2) 848
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 389592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 125756
32.3%
e 85028
21.8%
o 73317
18.8%
m 23445
 
6.0%
d 23445
 
6.0%
i 13006
 
3.3%
l 13006
 
3.3%
r 10863
 
2.8%
a 10439
 
2.7%
t 10439
 
2.7%
Other values (2) 848
 
0.2%

PreopEGFRMDRD
Real number (ℝ)

MISSING 

Distinct14893
Distinct (%)18.6%
Missing10830
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean96.426155
Minimum2.5410256
Maximum671.29815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:48.211974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.5410256
5-th percentile37.645463
Q179.078715
median96.398561
Q3114.31017
95-th percentile148.14637
Maximum671.29815
Range668.75712
Interquartile range (IQR)35.231454

Descriptive statistics

Standard deviation33.954241
Coefficient of variation (CV)0.35212688
Kurtosis6.2844048
Mean96.426155
Median Absolute Deviation (MAD)17.63906
Skewness0.56168938
Sum7709753.2
Variance1152.8905
MonotonicityNot monotonic
2024-08-02T16:32:48.260433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.4525274 50
 
0.1%
105.0271605 49
 
0.1%
104.6570134 46
 
0.1%
111.2684265 46
 
0.1%
95.52951776 45
 
< 0.1%
94.3057407 45
 
< 0.1%
102.5033118 44
 
< 0.1%
94.60302144 43
 
< 0.1%
109.6139465 43
 
< 0.1%
103.614409 43
 
< 0.1%
Other values (14883) 79501
87.6%
(Missing) 10830
 
11.9%
ValueCountFrequency (%)
2.54102559 1
< 0.1%
2.639693957 1
< 0.1%
2.738846918 1
< 0.1%
2.745064497 1
< 0.1%
2.763720271 1
< 0.1%
2.948184562 1
< 0.1%
3.109086732 1
< 0.1%
3.149290312 1
< 0.1%
3.257128712 1
< 0.1%
3.381878373 1
< 0.1%
ValueCountFrequency (%)
671.2981473 1
< 0.1%
588.4665784 1
< 0.1%
503.5882855 1
< 0.1%
481.6349276 1
< 0.1%
469.1914885 1
< 0.1%
458.2553477 1
< 0.1%
455.2188795 1
< 0.1%
449.5095212 1
< 0.1%
439.7856416 1
< 0.1%
438.0170254 1
< 0.1%

GradeofKidneydisease
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
g1
47948 
G2
23635 
BLANK
10830 
G3a
 
3425
G5
 
2059
Other values (2)
 
2888

Length

Max length5
Median length2
Mean length2.4142094
Min length2

Characters and Unicode

Total characters219174
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLANK
2nd rowBLANK
3rd rowg1
4th rowg1
5th rowg1

Common Values

ValueCountFrequency (%)
g1 47948
52.8%
G2 23635
26.0%
BLANK 10830
 
11.9%
G3a 3425
 
3.8%
G5 2059
 
2.3%
G3b 1689
 
1.9%
G4 1199
 
1.3%

Length

2024-08-02T16:32:48.308598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:48.346430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
g1 47948
52.8%
g2 23635
26.0%
blank 10830
 
11.9%
g3a 3425
 
3.8%
g5 2059
 
2.3%
g3b 1689
 
1.9%
g4 1199
 
1.3%

Most occurring characters

ValueCountFrequency (%)
g 47948
21.9%
1 47948
21.9%
G 32007
14.6%
2 23635
10.8%
B 10830
 
4.9%
L 10830
 
4.9%
A 10830
 
4.9%
N 10830
 
4.9%
K 10830
 
4.9%
3 5114
 
2.3%
Other values (4) 8372
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 219174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g 47948
21.9%
1 47948
21.9%
G 32007
14.6%
2 23635
10.8%
B 10830
 
4.9%
L 10830
 
4.9%
A 10830
 
4.9%
N 10830
 
4.9%
K 10830
 
4.9%
3 5114
 
2.3%
Other values (4) 8372
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 219174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g 47948
21.9%
1 47948
21.9%
G 32007
14.6%
2 23635
10.8%
B 10830
 
4.9%
L 10830
 
4.9%
A 10830
 
4.9%
N 10830
 
4.9%
K 10830
 
4.9%
3 5114
 
2.3%
Other values (4) 8372
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 219174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g 47948
21.9%
1 47948
21.9%
G 32007
14.6%
2 23635
10.8%
B 10830
 
4.9%
L 10830
 
4.9%
A 10830
 
4.9%
N 10830
 
4.9%
K 10830
 
4.9%
3 5114
 
2.3%
Other values (4) 8372
 
3.8%

DaysbetweenDeathandoperation
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1408
Distinct (%)25.2%
Missing85190
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean476.78213
Minimum0
Maximum1783
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:48.390648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q1121
median355
Q3746
95-th percentile1319
Maximum1783
Range1783
Interquartile range (IQR)625

Descriptive statistics

Standard deviation421.86674
Coefficient of variation (CV)0.88482079
Kurtosis-0.13364877
Mean476.78213
Median Absolute Deviation (MAD)273
Skewness0.88829654
Sum2667596
Variance177971.55
MonotonicityNot monotonic
2024-08-02T16:32:48.439148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 33
 
< 0.1%
7 26
 
< 0.1%
4 24
 
< 0.1%
3 23
 
< 0.1%
6 23
 
< 0.1%
5 22
 
< 0.1%
13 22
 
< 0.1%
0 21
 
< 0.1%
15 20
 
< 0.1%
32 20
 
< 0.1%
Other values (1398) 5361
 
5.9%
(Missing) 85190
93.8%
ValueCountFrequency (%)
0 21
< 0.1%
1 33
< 0.1%
2 18
< 0.1%
3 23
< 0.1%
4 24
< 0.1%
5 22
< 0.1%
6 23
< 0.1%
7 26
< 0.1%
8 15
< 0.1%
9 20
< 0.1%
ValueCountFrequency (%)
1783 1
< 0.1%
1749 1
< 0.1%
1739 2
< 0.1%
1735 1
< 0.1%
1730 2
< 0.1%
1722 1
< 0.1%
1713 1
< 0.1%
1707 1
< 0.1%
1703 1
< 0.1%
1700 1
< 0.1%

@30daymortality
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.8 KiB
False
90246 
True
 
539
ValueCountFrequency (%)
False 90246
99.4%
True 539
 
0.6%
2024-08-02T16:32:48.476417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Preoptransfusionwithin30days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.042672248
Minimum0
Maximum21
Zeros88905
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:48.573713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40584012
Coefficient of variation (CV)9.5106337
Kurtosis483.68275
Mean0.042672248
Median Absolute Deviation (MAD)0
Skewness17.960112
Sum3874
Variance0.1647062
MonotonicityNot monotonic
2024-08-02T16:32:48.609390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 88905
97.9%
1 1017
 
1.1%
2 460
 
0.5%
3 182
 
0.2%
4 74
 
0.1%
5 47
 
0.1%
6 24
 
< 0.1%
7 23
 
< 0.1%
8 19
 
< 0.1%
11 8
 
< 0.1%
Other values (8) 26
 
< 0.1%
ValueCountFrequency (%)
0 88905
97.9%
1 1017
 
1.1%
2 460
 
0.5%
3 182
 
0.2%
4 74
 
0.1%
5 47
 
0.1%
6 24
 
< 0.1%
7 23
 
< 0.1%
8 19
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
17 1
 
< 0.1%
14 4
 
< 0.1%
13 5
 
< 0.1%
12 1
 
< 0.1%
11 8
< 0.1%
10 7
 
< 0.1%
9 6
 
< 0.1%
8 19
< 0.1%

Intraop
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
0.0
85676 
1.0
 
5109

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272355
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 85676
94.4%
1.0 5109
 
5.6%

Length

2024-08-02T16:32:48.646287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:48.675324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85676
94.4%
1.0 5109
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 176461
64.8%
. 90785
33.3%
1 5109
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 272355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 176461
64.8%
. 90785
33.3%
1 5109
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 272355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 176461
64.8%
. 90785
33.3%
1 5109
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 272355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 176461
64.8%
. 90785
33.3%
1 5109
 
1.9%

Postopwithin30days
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018945861
Minimum0
Maximum23
Zeros89842
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:48.704255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26072799
Coefficient of variation (CV)13.761739
Kurtosis1484.6742
Mean0.018945861
Median Absolute Deviation (MAD)0
Skewness29.854492
Sum1720
Variance0.067979084
MonotonicityNot monotonic
2024-08-02T16:32:48.739383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 89842
99.0%
1 608
 
0.7%
2 179
 
0.2%
3 62
 
0.1%
4 34
 
< 0.1%
5 23
 
< 0.1%
6 12
 
< 0.1%
7 9
 
< 0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
Other values (5) 8
 
< 0.1%
ValueCountFrequency (%)
0 89842
99.0%
1 608
 
0.7%
2 179
 
0.2%
3 62
 
0.1%
4 34
 
< 0.1%
5 23
 
< 0.1%
6 12
 
< 0.1%
7 9
 
< 0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
23 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
12 3
 
< 0.1%
10 1
 
< 0.1%
9 3
 
< 0.1%
8 5
 
< 0.1%
7 9
 
< 0.1%
6 12
< 0.1%
5 23
< 0.1%

Transfusionintraandpostop
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075221678
Minimum0
Maximum24
Zeros85664
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size709.4 KiB
2024-08-02T16:32:48.773655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39562329
Coefficient of variation (CV)5.259432
Kurtosis378.07256
Mean0.075221678
Median Absolute Deviation (MAD)0
Skewness13.14898
Sum6829
Variance0.15651779
MonotonicityNot monotonic
2024-08-02T16:32:48.810935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 85664
94.4%
1 4187
 
4.6%
2 602
 
0.7%
3 176
 
0.2%
4 62
 
0.1%
5 34
 
< 0.1%
6 23
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 5
 
< 0.1%
Other values (6) 11
 
< 0.1%
ValueCountFrequency (%)
0 85664
94.4%
1 4187
 
4.6%
2 602
 
0.7%
3 176
 
0.2%
4 62
 
0.1%
5 34
 
< 0.1%
6 23
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
13 3
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 5
 
< 0.1%
8 9
 
< 0.1%
7 12
< 0.1%
6 23
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
GA
76442 
RA
14343 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters181570
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGA
2nd rowGA
3rd rowGA
4th rowGA
5th rowGA

Common Values

ValueCountFrequency (%)
GA 76442
84.2%
RA 14343
 
15.8%

Length

2024-08-02T16:32:48.847859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:48.877841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ga 76442
84.2%
ra 14343
 
15.8%

Most occurring characters

ValueCountFrequency (%)
A 90785
50.0%
G 76442
42.1%
R 14343
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 90785
50.0%
G 76442
42.1%
R 14343
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 90785
50.0%
G 76442
42.1%
R 14343
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 90785
50.0%
G 76442
42.1%
R 14343
 
7.9%

PriorityCategory
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
Elective
72331 
Emergency
18454 

Length

Max length9
Median length8
Mean length8.2032715
Min length8

Characters and Unicode

Total characters744734
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElective
2nd rowElective
3rd rowElective
4th rowElective
5th rowElective

Common Values

ValueCountFrequency (%)
Elective 72331
79.7%
Emergency 18454
 
20.3%

Length

2024-08-02T16:32:48.913290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:48.945327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
elective 72331
79.7%
emergency 18454
 
20.3%

Most occurring characters

ValueCountFrequency (%)
e 181570
24.4%
E 90785
12.2%
c 90785
12.2%
l 72331
 
9.7%
t 72331
 
9.7%
i 72331
 
9.7%
v 72331
 
9.7%
m 18454
 
2.5%
r 18454
 
2.5%
g 18454
 
2.5%
Other values (2) 36908
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 181570
24.4%
E 90785
12.2%
c 90785
12.2%
l 72331
 
9.7%
t 72331
 
9.7%
i 72331
 
9.7%
v 72331
 
9.7%
m 18454
 
2.5%
r 18454
 
2.5%
g 18454
 
2.5%
Other values (2) 36908
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 181570
24.4%
E 90785
12.2%
c 90785
12.2%
l 72331
 
9.7%
t 72331
 
9.7%
i 72331
 
9.7%
v 72331
 
9.7%
m 18454
 
2.5%
r 18454
 
2.5%
g 18454
 
2.5%
Other values (2) 36908
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 181570
24.4%
E 90785
12.2%
c 90785
12.2%
l 72331
 
9.7%
t 72331
 
9.7%
i 72331
 
9.7%
v 72331
 
9.7%
m 18454
 
2.5%
r 18454
 
2.5%
g 18454
 
2.5%
Other values (2) 36908
 
5.0%

TransfusionIntraandpostopCategory
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
0 units
85664 
1 unit
 
4187
2 or more units
 
934

Length

Max length15
Median length7
Mean length7.0361844
Min length6

Characters and Unicode

Total characters638780
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0 units
2nd row0 units
3rd row0 units
4th row1 unit
5th row0 units

Common Values

ValueCountFrequency (%)
0 units 85664
94.4%
1 unit 4187
 
4.6%
2 or more units 934
 
1.0%

Length

2024-08-02T16:32:48.979190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.009379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
units 86598
47.2%
0 85664
46.7%
1 4187
 
2.3%
unit 4187
 
2.3%
2 934
 
0.5%
or 934
 
0.5%
more 934
 
0.5%

Most occurring characters

ValueCountFrequency (%)
92653
14.5%
u 90785
14.2%
n 90785
14.2%
i 90785
14.2%
t 90785
14.2%
s 86598
13.6%
0 85664
13.4%
1 4187
 
0.7%
o 1868
 
0.3%
r 1868
 
0.3%
Other values (3) 2802
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 638780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
92653
14.5%
u 90785
14.2%
n 90785
14.2%
i 90785
14.2%
t 90785
14.2%
s 86598
13.6%
0 85664
13.4%
1 4187
 
0.7%
o 1868
 
0.3%
r 1868
 
0.3%
Other values (3) 2802
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 638780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
92653
14.5%
u 90785
14.2%
n 90785
14.2%
i 90785
14.2%
t 90785
14.2%
s 86598
13.6%
0 85664
13.4%
1 4187
 
0.7%
o 1868
 
0.3%
r 1868
 
0.3%
Other values (3) 2802
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 638780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
92653
14.5%
u 90785
14.2%
n 90785
14.2%
i 90785
14.2%
t 90785
14.2%
s 86598
13.6%
0 85664
13.4%
1 4187
 
0.7%
o 1868
 
0.3%
r 1868
 
0.3%
Other values (3) 2802
 
0.4%

AGEcategory
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
50-64
28227 
30-49
27078 
65-74
15837 
18-29
11052 
75-84
7256 

Length

Max length5
Median length5
Mean length4.9852949
Min length4

Characters and Unicode

Total characters452590
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-49
2nd row30-49
3rd row50-64
4th row65-74
5th row65-74

Common Values

ValueCountFrequency (%)
50-64 28227
31.1%
30-49 27078
29.8%
65-74 15837
17.4%
18-29 11052
 
12.2%
75-84 7256
 
8.0%
>=85 1335
 
1.5%

Length

2024-08-02T16:32:49.044626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.079620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
50-64 28227
31.1%
30-49 27078
29.8%
65-74 15837
17.4%
18-29 11052
 
12.2%
75-84 7256
 
8.0%
85 1335
 
1.5%

Most occurring characters

ValueCountFrequency (%)
- 89450
19.8%
4 78398
17.3%
0 55305
12.2%
5 52655
11.6%
6 44064
9.7%
9 38130
8.4%
3 27078
 
6.0%
7 23093
 
5.1%
8 19643
 
4.3%
1 11052
 
2.4%
Other values (3) 13722
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 452590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 89450
19.8%
4 78398
17.3%
0 55305
12.2%
5 52655
11.6%
6 44064
9.7%
9 38130
8.4%
3 27078
 
6.0%
7 23093
 
5.1%
8 19643
 
4.3%
1 11052
 
2.4%
Other values (3) 13722
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 452590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 89450
19.8%
4 78398
17.3%
0 55305
12.2%
5 52655
11.6%
6 44064
9.7%
9 38130
8.4%
3 27078
 
6.0%
7 23093
 
5.1%
8 19643
 
4.3%
1 11052
 
2.4%
Other values (3) 13722
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 452590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 89450
19.8%
4 78398
17.3%
0 55305
12.2%
5 52655
11.6%
6 44064
9.7%
9 38130
8.4%
3 27078
 
6.0%
7 23093
 
5.1%
8 19643
 
4.3%
1 11052
 
2.4%
Other values (3) 13722
 
3.0%

AGEcategoryOriginal
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
50-69
37360 
30-49
27078 
>=70
15295 
18-29
11052 

Length

Max length5
Median length5
Mean length4.831525
Min length4

Characters and Unicode

Total characters438630
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-49
2nd row30-49
3rd row50-69
4th row>=70
5th row>=70

Common Values

ValueCountFrequency (%)
50-69 37360
41.2%
30-49 27078
29.8%
>=70 15295
16.8%
18-29 11052
 
12.2%

Length

2024-08-02T16:32:49.122511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.156956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
50-69 37360
41.2%
30-49 27078
29.8%
70 15295
16.8%
18-29 11052
 
12.2%

Most occurring characters

ValueCountFrequency (%)
0 79733
18.2%
- 75490
17.2%
9 75490
17.2%
5 37360
8.5%
6 37360
8.5%
3 27078
 
6.2%
4 27078
 
6.2%
> 15295
 
3.5%
= 15295
 
3.5%
7 15295
 
3.5%
Other values (3) 33156
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79733
18.2%
- 75490
17.2%
9 75490
17.2%
5 37360
8.5%
6 37360
8.5%
3 27078
 
6.2%
4 27078
 
6.2%
> 15295
 
3.5%
= 15295
 
3.5%
7 15295
 
3.5%
Other values (3) 33156
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79733
18.2%
- 75490
17.2%
9 75490
17.2%
5 37360
8.5%
6 37360
8.5%
3 27078
 
6.2%
4 27078
 
6.2%
> 15295
 
3.5%
= 15295
 
3.5%
7 15295
 
3.5%
Other values (3) 33156
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79733
18.2%
- 75490
17.2%
9 75490
17.2%
5 37360
8.5%
6 37360
8.5%
3 27078
 
6.2%
4 27078
 
6.2%
> 15295
 
3.5%
= 15295
 
3.5%
7 15295
 
3.5%
Other values (3) 33156
7.6%

Mortality
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
No death
85190 
Yes
 
5595

Length

Max length8
Median length8
Mean length7.6918544
Min length3

Characters and Unicode

Total characters698305
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo death
2nd rowNo death
3rd rowNo death
4th rowNo death
5th rowYes

Common Values

ValueCountFrequency (%)
No death 85190
93.8%
Yes 5595
 
6.2%

Length

2024-08-02T16:32:49.195578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.225454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 85190
48.4%
death 85190
48.4%
yes 5595
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 90785
13.0%
N 85190
12.2%
o 85190
12.2%
85190
12.2%
d 85190
12.2%
a 85190
12.2%
t 85190
12.2%
h 85190
12.2%
Y 5595
 
0.8%
s 5595
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 698305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90785
13.0%
N 85190
12.2%
o 85190
12.2%
85190
12.2%
d 85190
12.2%
a 85190
12.2%
t 85190
12.2%
h 85190
12.2%
Y 5595
 
0.8%
s 5595
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 698305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90785
13.0%
N 85190
12.2%
o 85190
12.2%
85190
12.2%
d 85190
12.2%
a 85190
12.2%
t 85190
12.2%
h 85190
12.2%
Y 5595
 
0.8%
s 5595
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 698305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90785
13.0%
N 85190
12.2%
o 85190
12.2%
85190
12.2%
d 85190
12.2%
a 85190
12.2%
t 85190
12.2%
h 85190
12.2%
Y 5595
 
0.8%
s 5595
 
0.8%

thirtydaymortality
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.8 KiB
False
90246 
True
 
539
ValueCountFrequency (%)
False 90246
99.4%
True 539
 
0.6%
2024-08-02T16:32:49.251347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

SurgRiskCategory
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
Low
48049 
Moderate
39014 
High
 
3722

Length

Max length8
Median length3
Mean length5.1897009
Min length3

Characters and Unicode

Total characters471147
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowModerate
5th rowLow

Common Values

ValueCountFrequency (%)
Low 48049
52.9%
Moderate 39014
43.0%
High 3722
 
4.1%

Length

2024-08-02T16:32:49.285713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.320235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
low 48049
52.9%
moderate 39014
43.0%
high 3722
 
4.1%

Most occurring characters

ValueCountFrequency (%)
o 87063
18.5%
e 78028
16.6%
L 48049
10.2%
w 48049
10.2%
M 39014
8.3%
d 39014
8.3%
r 39014
8.3%
a 39014
8.3%
t 39014
8.3%
H 3722
 
0.8%
Other values (3) 11166
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 471147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 87063
18.5%
e 78028
16.6%
L 48049
10.2%
w 48049
10.2%
M 39014
8.3%
d 39014
8.3%
r 39014
8.3%
a 39014
8.3%
t 39014
8.3%
H 3722
 
0.8%
Other values (3) 11166
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 471147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 87063
18.5%
e 78028
16.6%
L 48049
10.2%
w 48049
10.2%
M 39014
8.3%
d 39014
8.3%
r 39014
8.3%
a 39014
8.3%
t 39014
8.3%
H 3722
 
0.8%
Other values (3) 11166
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 471147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 87063
18.5%
e 78028
16.6%
L 48049
10.2%
w 48049
10.2%
M 39014
8.3%
d 39014
8.3%
r 39014
8.3%
a 39014
8.3%
t 39014
8.3%
H 3722
 
0.8%
Other values (3) 11166
 
2.4%

RaceCategory
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
Chinese
64861 
Malay
8979 
Others
8927 
Indian
8012 
#NULL!
 
6

Length

Max length7
Median length7
Mean length6.6155422
Min length5

Characters and Unicode

Total characters600592
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChinese
2nd rowChinese
3rd rowChinese
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Chinese 64861
71.4%
Malay 8979
 
9.9%
Others 8927
 
9.8%
Indian 8012
 
8.8%
#NULL! 6
 
< 0.1%

Length

2024-08-02T16:32:49.362213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.400871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
chinese 64861
71.4%
malay 8979
 
9.9%
others 8927
 
9.8%
indian 8012
 
8.8%
null 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 138649
23.1%
n 80885
13.5%
s 73788
12.3%
h 73788
12.3%
i 72873
12.1%
C 64861
10.8%
a 25970
 
4.3%
M 8979
 
1.5%
l 8979
 
1.5%
y 8979
 
1.5%
Other values (10) 42841
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 138649
23.1%
n 80885
13.5%
s 73788
12.3%
h 73788
12.3%
i 72873
12.1%
C 64861
10.8%
a 25970
 
4.3%
M 8979
 
1.5%
l 8979
 
1.5%
y 8979
 
1.5%
Other values (10) 42841
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 138649
23.1%
n 80885
13.5%
s 73788
12.3%
h 73788
12.3%
i 72873
12.1%
C 64861
10.8%
a 25970
 
4.3%
M 8979
 
1.5%
l 8979
 
1.5%
y 8979
 
1.5%
Other values (10) 42841
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 138649
23.1%
n 80885
13.5%
s 73788
12.3%
h 73788
12.3%
i 72873
12.1%
C 64861
10.8%
a 25970
 
4.3%
M 8979
 
1.5%
l 8979
 
1.5%
y 8979
 
1.5%
Other values (10) 42841
 
7.1%

CVARCRICategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
no
60917 
#NULL!
28325 
yes
 
1543

Length

Max length6
Median length2
Mean length3.2649997
Min length2

Characters and Unicode

Total characters296413
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd row#NULL!
3rd row#NULL!
4th row#NULL!
5th rowno

Common Values

ValueCountFrequency (%)
no 60917
67.1%
#NULL! 28325
31.2%
yes 1543
 
1.7%

Length

2024-08-02T16:32:49.445926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.481225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 60917
67.1%
null 28325
31.2%
yes 1543
 
1.7%

Most occurring characters

ValueCountFrequency (%)
n 60917
20.6%
o 60917
20.6%
L 56650
19.1%
# 28325
9.6%
N 28325
9.6%
U 28325
9.6%
! 28325
9.6%
y 1543
 
0.5%
e 1543
 
0.5%
s 1543
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 296413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 60917
20.6%
o 60917
20.6%
L 56650
19.1%
# 28325
9.6%
N 28325
9.6%
U 28325
9.6%
! 28325
9.6%
y 1543
 
0.5%
e 1543
 
0.5%
s 1543
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 296413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 60917
20.6%
o 60917
20.6%
L 56650
19.1%
# 28325
9.6%
N 28325
9.6%
U 28325
9.6%
! 28325
9.6%
y 1543
 
0.5%
e 1543
 
0.5%
s 1543
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 296413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 60917
20.6%
o 60917
20.6%
L 56650
19.1%
# 28325
9.6%
N 28325
9.6%
U 28325
9.6%
! 28325
9.6%
y 1543
 
0.5%
e 1543
 
0.5%
s 1543
 
0.5%

IHDRCRICategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
no
57968 
#NULL!
28572 
yes
 
4245

Length

Max length6
Median length2
Mean length3.3056452
Min length2

Characters and Unicode

Total characters300103
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd row#NULL!
3rd row#NULL!
4th row#NULL!
5th rowno

Common Values

ValueCountFrequency (%)
no 57968
63.9%
#NULL! 28572
31.5%
yes 4245
 
4.7%

Length

2024-08-02T16:32:49.521838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.557317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 57968
63.9%
null 28572
31.5%
yes 4245
 
4.7%

Most occurring characters

ValueCountFrequency (%)
n 57968
19.3%
o 57968
19.3%
L 57144
19.0%
# 28572
9.5%
N 28572
9.5%
U 28572
9.5%
! 28572
9.5%
y 4245
 
1.4%
e 4245
 
1.4%
s 4245
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 57968
19.3%
o 57968
19.3%
L 57144
19.0%
# 28572
9.5%
N 28572
9.5%
U 28572
9.5%
! 28572
9.5%
y 4245
 
1.4%
e 4245
 
1.4%
s 4245
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 57968
19.3%
o 57968
19.3%
L 57144
19.0%
# 28572
9.5%
N 28572
9.5%
U 28572
9.5%
! 28572
9.5%
y 4245
 
1.4%
e 4245
 
1.4%
s 4245
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 57968
19.3%
o 57968
19.3%
L 57144
19.0%
# 28572
9.5%
N 28572
9.5%
U 28572
9.5%
! 28572
9.5%
y 4245
 
1.4%
e 4245
 
1.4%
s 4245
 
1.4%

CHFRCRICategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
no
63739 
#NULL!
26259 
yes
 
787

Length

Max length6
Median length2
Mean length3.1656441
Min length2

Characters and Unicode

Total characters287393
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd row#NULL!
3rd row#NULL!
4th row#NULL!
5th rowno

Common Values

ValueCountFrequency (%)
no 63739
70.2%
#NULL! 26259
28.9%
yes 787
 
0.9%

Length

2024-08-02T16:32:49.597185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.633312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 63739
70.2%
null 26259
28.9%
yes 787
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 63739
22.2%
o 63739
22.2%
L 52518
18.3%
# 26259
9.1%
N 26259
9.1%
U 26259
9.1%
! 26259
9.1%
y 787
 
0.3%
e 787
 
0.3%
s 787
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 287393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 63739
22.2%
o 63739
22.2%
L 52518
18.3%
# 26259
9.1%
N 26259
9.1%
U 26259
9.1%
! 26259
9.1%
y 787
 
0.3%
e 787
 
0.3%
s 787
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 287393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 63739
22.2%
o 63739
22.2%
L 52518
18.3%
# 26259
9.1%
N 26259
9.1%
U 26259
9.1%
! 26259
9.1%
y 787
 
0.3%
e 787
 
0.3%
s 787
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 287393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 63739
22.2%
o 63739
22.2%
L 52518
18.3%
# 26259
9.1%
N 26259
9.1%
U 26259
9.1%
! 26259
9.1%
y 787
 
0.3%
e 787
 
0.3%
s 787
 
0.3%

DMinsulinRCRICategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
no
61907 
#NULL!
26875 
yes
 
2003

Length

Max length6
Median length2
Mean length3.2061794
Min length2

Characters and Unicode

Total characters291073
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd row#NULL!
3rd row#NULL!
4th row#NULL!
5th rowno

Common Values

ValueCountFrequency (%)
no 61907
68.2%
#NULL! 26875
29.6%
yes 2003
 
2.2%

Length

2024-08-02T16:32:49.673020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.709307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 61907
68.2%
null 26875
29.6%
yes 2003
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 61907
21.3%
o 61907
21.3%
L 53750
18.5%
# 26875
9.2%
N 26875
9.2%
U 26875
9.2%
! 26875
9.2%
y 2003
 
0.7%
e 2003
 
0.7%
s 2003
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 61907
21.3%
o 61907
21.3%
L 53750
18.5%
# 26875
9.2%
N 26875
9.2%
U 26875
9.2%
! 26875
9.2%
y 2003
 
0.7%
e 2003
 
0.7%
s 2003
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 61907
21.3%
o 61907
21.3%
L 53750
18.5%
# 26875
9.2%
N 26875
9.2%
U 26875
9.2%
! 26875
9.2%
y 2003
 
0.7%
e 2003
 
0.7%
s 2003
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 61907
21.3%
o 61907
21.3%
L 53750
18.5%
# 26875
9.2%
N 26875
9.2%
U 26875
9.2%
! 26875
9.2%
y 2003
 
0.7%
e 2003
 
0.7%
s 2003
 
0.7%

CreatinineRCRICategory
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
no
72760 
#NULL!
15743 
yes
 
2282

Length

Max length6
Median length2
Mean length2.7187751
Min length2

Characters and Unicode

Total characters246824
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd row#NULL!
3rd row#NULL!
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 72760
80.1%
#NULL! 15743
 
17.3%
yes 2282
 
2.5%

Length

2024-08-02T16:32:49.749740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.786337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 72760
80.1%
null 15743
 
17.3%
yes 2282
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n 72760
29.5%
o 72760
29.5%
L 31486
12.8%
# 15743
 
6.4%
N 15743
 
6.4%
U 15743
 
6.4%
! 15743
 
6.4%
y 2282
 
0.9%
e 2282
 
0.9%
s 2282
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 246824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 72760
29.5%
o 72760
29.5%
L 31486
12.8%
# 15743
 
6.4%
N 15743
 
6.4%
U 15743
 
6.4%
! 15743
 
6.4%
y 2282
 
0.9%
e 2282
 
0.9%
s 2282
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 246824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 72760
29.5%
o 72760
29.5%
L 31486
12.8%
# 15743
 
6.4%
N 15743
 
6.4%
U 15743
 
6.4%
! 15743
 
6.4%
y 2282
 
0.9%
e 2282
 
0.9%
s 2282
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 246824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 72760
29.5%
o 72760
29.5%
L 31486
12.8%
# 15743
 
6.4%
N 15743
 
6.4%
U 15743
 
6.4%
! 15743
 
6.4%
y 2282
 
0.9%
e 2282
 
0.9%
s 2282
 
0.9%

GradeofKidneyCategory
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
G1
47948 
G2
23635 
#NULL!
10830 
G3
5114 
G4-G5
 
3258

Length

Max length6
Median length2
Mean length2.5848323
Min length2

Characters and Unicode

Total characters234664
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd row#NULL!
3rd rowG1
4th rowG1
5th rowG1

Common Values

ValueCountFrequency (%)
G1 47948
52.8%
G2 23635
26.0%
#NULL! 10830
 
11.9%
G3 5114
 
5.6%
G4-G5 3258
 
3.6%

Length

2024-08-02T16:32:49.824981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.862577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
g1 47948
52.8%
g2 23635
26.0%
null 10830
 
11.9%
g3 5114
 
5.6%
g4-g5 3258
 
3.6%

Most occurring characters

ValueCountFrequency (%)
G 83213
35.5%
1 47948
20.4%
2 23635
 
10.1%
L 21660
 
9.2%
# 10830
 
4.6%
N 10830
 
4.6%
U 10830
 
4.6%
! 10830
 
4.6%
3 5114
 
2.2%
4 3258
 
1.4%
Other values (2) 6516
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 83213
35.5%
1 47948
20.4%
2 23635
 
10.1%
L 21660
 
9.2%
# 10830
 
4.6%
N 10830
 
4.6%
U 10830
 
4.6%
! 10830
 
4.6%
3 5114
 
2.2%
4 3258
 
1.4%
Other values (2) 6516
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 83213
35.5%
1 47948
20.4%
2 23635
 
10.1%
L 21660
 
9.2%
# 10830
 
4.6%
N 10830
 
4.6%
U 10830
 
4.6%
! 10830
 
4.6%
3 5114
 
2.2%
4 3258
 
1.4%
Other values (2) 6516
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 83213
35.5%
1 47948
20.4%
2 23635
 
10.1%
L 21660
 
9.2%
# 10830
 
4.6%
N 10830
 
4.6%
U 10830
 
4.6%
! 10830
 
4.6%
3 5114
 
2.2%
4 3258
 
1.4%
Other values (2) 6516
 
2.8%

Anemiacategorybinned
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing62878
Missing (%)69.3%
Memory size709.4 KiB
Mild
13006 
Moderate/Severe
10863 
#NULL!
4038 

Length

Max length15
Median length6
Mean length8.5712187
Min length4

Characters and Unicode

Total characters239197
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd rowMild
3rd rowModerate/Severe
4th rowMild
5th rowModerate/Severe

Common Values

ValueCountFrequency (%)
Mild 13006
 
14.3%
Moderate/Severe 10863
 
12.0%
#NULL! 4038
 
4.4%
(Missing) 62878
69.3%

Length

2024-08-02T16:32:49.902812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:49.937380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
mild 13006
46.6%
moderate/severe 10863
38.9%
null 4038
 
14.5%

Most occurring characters

ValueCountFrequency (%)
e 54315
22.7%
M 23869
10.0%
d 23869
10.0%
r 21726
 
9.1%
l 13006
 
5.4%
i 13006
 
5.4%
/ 10863
 
4.5%
v 10863
 
4.5%
S 10863
 
4.5%
t 10863
 
4.5%
Other values (7) 45954
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 54315
22.7%
M 23869
10.0%
d 23869
10.0%
r 21726
 
9.1%
l 13006
 
5.4%
i 13006
 
5.4%
/ 10863
 
4.5%
v 10863
 
4.5%
S 10863
 
4.5%
t 10863
 
4.5%
Other values (7) 45954
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 54315
22.7%
M 23869
10.0%
d 23869
10.0%
r 21726
 
9.1%
l 13006
 
5.4%
i 13006
 
5.4%
/ 10863
 
4.5%
v 10863
 
4.5%
S 10863
 
4.5%
t 10863
 
4.5%
Other values (7) 45954
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 54315
22.7%
M 23869
10.0%
d 23869
10.0%
r 21726
 
9.1%
l 13006
 
5.4%
i 13006
 
5.4%
/ 10863
 
4.5%
v 10863
 
4.5%
S 10863
 
4.5%
t 10863
 
4.5%
Other values (7) 45954
19.2%

RDW15.7
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
<= 15.7
76069 
>15.7
8478 
#NULL!
 
6238

Length

Max length7
Median length7
Mean length6.7445173
Min length5

Characters and Unicode

Total characters612301
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row#NULL!
2nd row<= 15.7
3rd row<= 15.7
4th row<= 15.7
5th row>15.7

Common Values

ValueCountFrequency (%)
<= 15.7 76069
83.8%
>15.7 8478
 
9.3%
#NULL! 6238
 
6.9%

Length

2024-08-02T16:32:49.979743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:50.016060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
15.7 84547
50.7%
76069
45.6%
null 6238
 
3.7%

Most occurring characters

ValueCountFrequency (%)
1 84547
13.8%
5 84547
13.8%
. 84547
13.8%
7 84547
13.8%
< 76069
12.4%
= 76069
12.4%
76069
12.4%
L 12476
 
2.0%
> 8478
 
1.4%
# 6238
 
1.0%
Other values (3) 18714
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 612301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 84547
13.8%
5 84547
13.8%
. 84547
13.8%
7 84547
13.8%
< 76069
12.4%
= 76069
12.4%
76069
12.4%
L 12476
 
2.0%
> 8478
 
1.4%
# 6238
 
1.0%
Other values (3) 18714
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 612301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 84547
13.8%
5 84547
13.8%
. 84547
13.8%
7 84547
13.8%
< 76069
12.4%
= 76069
12.4%
76069
12.4%
L 12476
 
2.0%
> 8478
 
1.4%
# 6238
 
1.0%
Other values (3) 18714
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 612301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 84547
13.8%
5 84547
13.8%
. 84547
13.8%
7 84547
13.8%
< 76069
12.4%
= 76069
12.4%
76069
12.4%
L 12476
 
2.0%
> 8478
 
1.4%
# 6238
 
1.0%
Other values (3) 18714
 
3.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size709.4 KiB
II
49435 
I
22047 
III
13405 
#NULL!
 
4819
IV-VI
 
1079

Length

Max length6
Median length2
Mean length2.1527896
Min length1

Characters and Unicode

Total characters195441
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowI
3rd rowI
4th rowI
5th rowII

Common Values

ValueCountFrequency (%)
II 49435
54.5%
I 22047
24.3%
III 13405
 
14.8%
#NULL! 4819
 
5.3%
IV-VI 1079
 
1.2%

Length

2024-08-02T16:32:50.053638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T16:32:50.088617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ii 49435
54.5%
i 22047
24.3%
iii 13405
 
14.8%
null 4819
 
5.3%
iv-vi 1079
 
1.2%

Most occurring characters

ValueCountFrequency (%)
I 163290
83.5%
L 9638
 
4.9%
# 4819
 
2.5%
N 4819
 
2.5%
U 4819
 
2.5%
! 4819
 
2.5%
V 2158
 
1.1%
- 1079
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 163290
83.5%
L 9638
 
4.9%
# 4819
 
2.5%
N 4819
 
2.5%
U 4819
 
2.5%
! 4819
 
2.5%
V 2158
 
1.1%
- 1079
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 163290
83.5%
L 9638
 
4.9%
# 4819
 
2.5%
N 4819
 
2.5%
U 4819
 
2.5%
! 4819
 
2.5%
V 2158
 
1.1%
- 1079
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 163290
83.5%
L 9638
 
4.9%
# 4819
 
2.5%
N 4819
 
2.5%
U 4819
 
2.5%
! 4819
 
2.5%
V 2158
 
1.1%
- 1079
 
0.6%

ICUAdmgt24h
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.8 KiB
False
89521 
True
 
1264
ValueCountFrequency (%)
False 89521
98.6%
True 1264
 
1.4%
2024-08-02T16:32:50.121189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-08-02T16:32:46.733747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.068171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.417584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.665597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.919560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.168582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.419952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.767487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.126769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.451477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.700319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.953027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.201186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.452540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.803360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.191890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.486608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.737422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.988236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.238594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.489404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.839516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.249352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.523519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.774202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.024362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.274390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.524097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.877689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.306185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.560911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.812622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.062618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.313114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.561880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.912154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.349110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.596049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.849260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.096222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.346890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.596494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.947625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.382245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.630349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:45.884610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.129525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.382114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-02T16:32:46.629907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-08-02T16:32:50.156545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
@30daymortalityAGEAGEcategoryAGEcategoryOriginalASAcategorybinnedAnaestypeCategoryAnemia categoryAnemiacategorybinnedCHFRCRICategoryCVARCRICategoryCreatinineRCRICategoryDMinsulinRCRICategoryDaysbetweenDeathandoperationGENDERGradeofKidneyCategoryGradeofKidneydiseaseICUAdmgt24hIHDRCRICategoryIntraopMortalityPostopwithin30daysPreopEGFRMDRDPreoptransfusionwithin30daysPriorityCategoryRCRI scoreRDW15.7RaceCategorySurgRiskCategoryTransfusionIntraandpostopCategoryTransfusionintraandpostopthirtydaymortality
@30daymortality1.0000.0960.0970.0780.2110.0000.1290.0930.0500.0400.0940.0320.4720.0170.1310.1360.2040.0730.1130.3010.1170.1190.1130.0880.1390.0860.0080.0560.1300.1140.999
AGE0.0961.0000.7980.9100.2510.2580.1260.2930.0890.1070.0880.078-0.0360.0910.2590.2130.0780.1510.1240.2310.060-0.4390.0800.1690.2340.1240.1340.1410.0900.1020.096
AGEcategory0.0970.7981.0000.9390.2430.2420.1220.2770.0870.1030.0850.0750.0340.0790.2440.2200.0760.1470.1200.2240.0140.1690.0170.1410.1110.1130.1270.1370.0860.0220.097
AGEcategoryOriginal0.0780.9100.9391.0000.2740.2250.1100.2750.0800.0990.0810.0730.0460.0770.2750.2770.0740.1390.1050.2080.0150.2100.0210.1280.1360.1120.1430.1320.0760.0260.078
ASAcategorybinned0.2110.2510.2430.2741.0000.1560.1850.2740.2840.2690.3810.2830.1310.0720.2450.2470.2380.3330.1800.3070.0500.1890.0720.2200.2620.1270.0650.1490.1350.0590.211
AnaestypeCategory0.0000.2580.2420.2250.1561.0000.0970.1590.0610.0560.0880.0730.0610.0550.1610.1620.0370.0870.0340.0420.0100.1580.0040.1340.0680.0930.0640.1530.0330.0120.000
Anemia category0.1290.1260.1220.1100.1850.0971.0001.0000.0650.0490.1730.0900.1150.1130.1960.1990.1050.0860.3610.2620.1180.1850.1060.1390.1650.3200.0340.0860.2840.1290.129
Anemiacategorybinned0.0930.2930.2770.2750.2740.1591.0001.0000.0910.0810.1450.1030.0990.1670.5460.5460.0780.0950.2820.1930.0580.1210.0660.2050.1830.6960.0830.1840.2080.0810.093
CHFRCRICategory0.0500.0890.0870.0800.2840.0610.0650.0911.0000.6700.4830.6970.0890.0210.1180.1200.0520.6850.0440.1040.0240.0980.0290.1000.3450.0400.0200.0390.0350.0230.050
CVARCRICategory0.0400.1070.1030.0990.2690.0560.0490.0810.6701.0000.4480.6570.1010.0380.0910.0920.0280.7090.0300.0860.0080.0750.0140.0790.2730.0200.0230.0370.0220.0110.040
CreatinineRCRICategory0.0940.0880.0850.0810.3810.0880.1730.1450.4830.4481.0000.4830.1090.0280.5280.5310.0600.4650.0640.1670.0220.2760.0500.1110.3670.0650.0410.0400.0520.0290.094
DMinsulinRCRICategory0.0320.0780.0750.0730.2830.0730.0900.1030.6970.6570.4831.0000.0830.0140.1520.1540.0240.6600.0350.0900.0060.1170.0160.1220.3300.0300.0420.0460.0270.0130.032
DaysbetweenDeathandoperation0.472-0.0360.0340.0460.1310.0610.1150.0990.0890.1010.1090.0831.0000.0320.0610.0560.2200.1070.1411.000-0.130-0.001-0.1710.197-0.1320.1190.0350.0650.110-0.1600.472
GENDER0.0170.0910.0790.0770.0720.0550.1130.1670.0210.0380.0280.0140.0321.0000.1670.1670.0370.0980.0280.0470.0120.1250.0140.0610.0720.0770.0490.1580.0290.0090.017
GradeofKidneyCategory0.1310.2590.2440.2750.2450.1610.1960.5460.1180.0910.5280.1520.0610.1671.0001.0000.1060.1630.1080.2580.0200.4860.0420.1180.2540.4900.0580.0770.0830.0290.131
GradeofKidneydisease0.1360.2130.2200.2770.2470.1620.1990.5460.1200.0920.5310.1540.0560.1671.0001.0000.1150.1640.1120.2600.0200.3770.0380.1210.2090.4900.0580.0780.0860.0270.136
ICUAdmgt24h0.2040.0780.0760.0740.2380.0370.1050.0780.0520.0280.0600.0240.2200.0370.1060.1151.0000.0620.1830.1560.0660.1100.2280.0770.1660.0530.0000.1800.1840.0760.204
IHDRCRICategory0.0730.1510.1470.1390.3330.0870.0860.0950.6850.7090.4650.6600.1070.0980.1630.1640.0621.0000.0510.1290.0190.1420.0260.0850.4370.0350.0190.0460.0370.0210.073
Intraop0.1130.1240.1200.1050.1800.0340.3610.2820.0440.0300.0640.0350.1410.0280.1080.1120.1830.0511.0000.1830.1700.1230.2680.0430.1860.1860.0290.2540.9990.2480.113
Mortality0.3010.2310.2240.2080.3070.0420.2620.1930.1040.0860.1670.0901.0000.0470.2580.2600.1560.1290.1831.0000.0870.2150.1130.1010.2590.1560.0600.1460.1920.1020.301
Postopwithin30days0.1170.0600.0140.0150.0500.0100.1180.0580.0240.0080.0220.006-0.1300.0120.0200.0200.0660.0190.1700.0871.000-0.0150.3000.0360.1040.0570.0000.0260.2880.4390.117
PreopEGFRMDRD0.119-0.4390.1690.2100.1890.1580.1850.1210.0980.0750.2760.117-0.0010.1250.4860.3770.1100.1420.1230.215-0.0151.000-0.0400.129-0.2110.0830.0360.0510.097-0.0280.119
Preoptransfusionwithin30days0.1130.0800.0170.0210.0720.0040.1060.0660.0290.0140.0500.016-0.1710.0140.0420.0380.2280.0260.2680.1130.300-0.0401.0000.0560.1350.0470.0080.0600.2080.5880.113
PriorityCategory0.0880.1690.1410.1280.2200.1340.1390.2050.1000.0790.1110.1220.1970.0610.1180.1210.0770.0850.0430.1010.0360.1290.0561.0000.1070.0840.1100.0210.0500.0420.088
RCRI score0.1390.2340.1110.1360.2620.0680.1650.1830.3450.2730.3670.330-0.1320.0720.2540.2090.1660.4370.1860.2590.104-0.2110.1350.1071.0000.0980.0190.2460.1340.1850.139
RDW15.70.0860.1240.1130.1120.1270.0930.3200.6960.0400.0200.0650.0300.1190.0770.4900.4900.0530.0350.1860.1560.0570.0830.0470.0840.0981.0000.0550.1040.1580.0800.086
RaceCategory0.0080.1340.1270.1430.0650.0640.0340.0830.0200.0230.0410.0420.0350.0490.0580.0580.0000.0190.0290.0600.0000.0360.0080.1100.0190.0551.0000.0510.0210.0000.008
SurgRiskCategory0.0560.1410.1370.1320.1490.1530.0860.1840.0390.0370.0400.0460.0650.1580.0770.0780.1800.0460.2540.1460.0260.0510.0600.0210.2460.1040.0511.0000.1800.0380.056
TransfusionIntraandpostopCategory0.1300.0900.0860.0760.1350.0330.2840.2080.0350.0220.0520.0270.1100.0290.0830.0860.1840.0370.9990.1920.2880.0970.2080.0500.1340.1580.0210.1801.0000.4200.130
Transfusionintraandpostop0.1140.1020.0220.0260.0590.0120.1290.0810.0230.0110.0290.013-0.1600.0090.0290.0270.0760.0210.2480.1020.439-0.0280.5880.0420.1850.0800.0000.0380.4201.0000.114
thirtydaymortality0.9990.0960.0970.0780.2110.0000.1290.0930.0500.0400.0940.0320.4720.0170.1310.1360.2040.0730.1130.3010.1170.1190.1130.0880.1390.0860.0080.0560.1300.1141.000

Missing values

2024-08-02T16:32:47.044355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-02T16:32:47.313236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-02T16:32:47.741941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AGEGENDERRCRI scoreAnemia categoryPreopEGFRMDRDGradeofKidneydiseaseDaysbetweenDeathandoperation@30daymortalityPreoptransfusionwithin30daysIntraopPostopwithin30daysTransfusionintraandpostopAnaestypeCategoryPriorityCategoryTransfusionIntraandpostopCategoryAGEcategoryAGEcategoryOriginalMortalitythirtydaymortalitySurgRiskCategoryRaceCategoryCVARCRICategoryIHDRCRICategoryCHFRCRICategoryDMinsulinRCRICategoryCreatinineRCRICategoryGradeofKidneyCategoryAnemiacategorybinnedRDW15.7ASAcategorybinnedICUAdmgt24h
048.0FEMALENaNNaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinese#NULL!#NULL!#NULL!#NULL!no#NULL!#NULL!#NULL!Ino
136.0FEMALENaNnoneNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinese#NULL!#NULL!#NULL!#NULL!#NULL!#NULL!NaN<= 15.7Ino
264.0FEMALENaNmild152.538570g1NaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoLowChinese#NULL!#NULL!#NULL!#NULL!#NULL!G1Mild<= 15.7Ino
373.0MALENaNmoderate117.231496g1NaNNO0.01.00.01.0GAElective1 unit65-74>=70No deathNoModerateChinese#NULL!#NULL!#NULL!#NULL!noG1Moderate/Severe<= 15.7Ino
473.0MALE0.0mild98.651255g159.0NO0.00.00.00.0GAElective0 units65-74>=70YesNoLowChinesenononononoG1Mild>15.7IIno
555.0MALENaNnone104.487306g1NaNNO0.00.00.00.0GAEmergency0 units50-6450-69No deathNoLowIndian#NULL!#NULL!#NULL!#NULL!#NULL!G1NaN<= 15.7Ino
648.0FEMALENaNnone72.669742G2NaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoModerateChinese#NULL!#NULL!#NULL!#NULL!#NULL!G2NaN<= 15.7IIno
779.0FEMALENaNnone130.581564g1NaNNO0.00.00.00.0GAElective0 units75-84>=70No deathNoHighChinese#NULL!#NULL!#NULL!#NULL!noG1NaN<= 15.7IIno
855.0FEMALE0.0none144.410728g1NaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoModerateChinese#NULL!#NULL!nononoG1NaN<= 15.7IIno
959.0MALE1.0none91.215796g1NaNNO0.00.00.00.0GAEmergency0 units50-6450-69No deathNoModerateChinesenononononoG1NaN<= 15.7Ino
AGEGENDERRCRI scoreAnemia categoryPreopEGFRMDRDGradeofKidneydiseaseDaysbetweenDeathandoperation@30daymortalityPreoptransfusionwithin30daysIntraopPostopwithin30daysTransfusionintraandpostopAnaestypeCategoryPriorityCategoryTransfusionIntraandpostopCategoryAGEcategoryAGEcategoryOriginalMortalitythirtydaymortalitySurgRiskCategoryRaceCategoryCVARCRICategoryIHDRCRICategoryCHFRCRICategoryDMinsulinRCRICategoryCreatinineRCRICategoryGradeofKidneyCategoryAnemiacategorybinnedRDW15.7ASAcategorybinnedICUAdmgt24h
9077561.0MALENaNnone60.204465G2NaNNO0.00.00.00.0GAEmergency0 units50-6450-69No deathNoModerateChinese#NULL!#NULL!#NULL!#NULL!noG2NaN<= 15.7IIno
9077652.0MALE0.0none86.091241G2NaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoLowOthersnononononoG2NaN<= 15.7IIno
9077753.0MALENaNnone74.637361G2NaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoLowOthers#NULL!#NULL!#NULL!#NULL!noG2NaN<= 15.7IIno
9077881.0FEMALE0.0none57.609630G3aNaNNO0.00.00.00.0GAElective0 units75-84>=70No deathNoLowOthersnononononoG3NaN<= 15.7IIno
9077946.0MALENaNNaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowOthers#NULL!#NULL!#NULL!#NULL!yes#NULL!#NULL!#NULL!IIIno
9078066.0FEMALE2.0mild48.339582G3aNaNNO0.00.00.00.0GAElective0 units65-7450-69No deathNoModerateOthersnononoyesnoG3Mild<= 15.7IIno
9078150.0MALE1.0moderate126.592489g1NaNNO0.00.00.00.0GAEmergency0 units50-6450-69No deathNoLowOthersnononoyesnoG1Moderate/Severe<= 15.7#NULL!no
9078258.0FEMALENaNnone86.306771G2NaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoLowOthers#NULL!#NULL!#NULL!#NULL!#NULL!G2NaN<= 15.7#NULL!no
9078363.0FEMALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoModerateChinesenonononono#NULL!#NULL!#NULL!IIno
9078445.0FEMALE0.0mild125.902498g1NaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoModerateChinesenononononoG1Mild<= 15.7IIno

Duplicate rows

Most frequently occurring

AGEGENDERRCRI scoreAnemia categoryPreopEGFRMDRDGradeofKidneydiseaseDaysbetweenDeathandoperation@30daymortalityPreoptransfusionwithin30daysIntraopPostopwithin30daysTransfusionintraandpostopAnaestypeCategoryPriorityCategoryTransfusionIntraandpostopCategoryAGEcategoryAGEcategoryOriginalMortalitythirtydaymortalitySurgRiskCategoryRaceCategoryCVARCRICategoryIHDRCRICategoryCHFRCRICategoryDMinsulinRCRICategoryCreatinineRCRICategoryGradeofKidneyCategoryAnemiacategorybinnedRDW15.7ASAcategorybinnedICUAdmgt24h# duplicates
15220.0MALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units18-2918-29No deathNoLowChinesenonononono#NULL!#NULL!#NULL!Ino27
24521.0MALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units18-2918-29No deathNoLowChinesenonononono#NULL!#NULL!#NULL!Ino22
113131.0FEMALE0.0noneNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinesenonononono#NULL!NaN<= 15.7Ino20
51725.0FEMALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units18-2918-29No deathNoLowChinesenonononono#NULL!#NULL!#NULL!Ino19
566964.0FEMALE0.0noneNaNBLANKNaNNO0.00.00.00.0GAElective0 units50-6450-69No deathNoLowChinesenonononono#NULL!NaN#NULL!IIno19
169835.0MALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinesenonononono#NULL!#NULL!#NULL!Ino17
32422.0MALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units18-2918-29No deathNoLowChinesenonononono#NULL!#NULL!#NULL!Ino16
100730.0FEMALE0.0noneNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinesenonononono#NULL!NaN<= 15.7Ino16
162735.0FEMALE0.0noneNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinesenonononono#NULL!NaN<= 15.7Ino16
209239.0FEMALE0.0NaNNaNBLANKNaNNO0.00.00.00.0GAElective0 units30-4930-49No deathNoLowChinesenonononono#NULL!#NULL!#NULL!Ino16